In [1]:
## 設定
verbose = False
### 言語の割合の均等化
balanced = True
### LDA 用
## トピック数
n_topics = 15 # 30は多過ぎる?
## doc, term の設定
doc_type = 'form'
doc_attr = 'sound'
max_doc_size = 12
##
term_size = 'character'
term_type = '2gram'
## skippy n-gram の結合範囲
max_distance_val = round(max_doc_size * 0.8)
print(f"max_distance_val: {max_distance_val}")
## ngram を包括的にするかどうか
ngram_is_inclusive = True
### DTM 構築
## term の最低頻度
term_min_freq = 2
## 高頻度 term の濫用指標: 大きくし過ぎないように.0.05 は十分に大きい
term_abuse_threshold = 0.05
max_distance_val: 10
In [2]:
import sys, os, random, re, glob
import pandas as pd
import pprint as pp
from functools import reduce
In [3]:
## load data to process
from pathlib import Path
import pprint as pp
wd = Path(".")
##
dirs = [ x for x in wd.iterdir() if x.is_dir() and not x.match(r"plot*") ]
if verbose:
print(f"The following {len(dirs)} directories are potential targets:")
pp.pprint(dirs)
## list up files in target directory
wd = Path(".")
target_dir = "data-words" # can be changed
target_files = sorted(list(wd.glob(f"{target_dir}/*.csv")))
#
print(f"\n{target_dir} contains {len(target_files)} files to process")
pp.pprint(target_files)
data-words contains 10 files to process
[PosixPath('data-words/base-sound-English-r6e-originals.csv'),
PosixPath('data-words/base-sound-French-r0-sample900.csv'),
PosixPath('data-words/base-sound-German-r1a-original.csv'),
PosixPath('data-words/base-spell-English-r6e-originals.csv'),
PosixPath('data-words/base-spell-Esperanto-r0-orginal.csv'),
PosixPath('data-words/base-spell-French-r0-originals.csv'),
PosixPath('data-words/base-spell-German-r1a-originals.csv'),
PosixPath('data-words/base-spell-Icelandic-r0-original.csv'),
PosixPath('data-words/base-spell-Russian-r0-originals.csv'),
PosixPath('data-words/base-spell-Swahili-r0-orginal.csv')]
In [4]:
import pandas as pd
## データ型の辞書
types = "spell sound freq".split(" ")
type_setting = { t : 0 for t in types }
print(type_setting)
## 言語名の辞書
langs = "english esperanto french german icelandic russian swahili".split(" ")
#langs = "english esperanto french german russian swahili".split(" ")
#langs = "english esperanto french german icelandic swahili".split(" ")
lang_setting = { lang : 0 for lang in langs }
print(lang_setting)
## 辞書と統合
settings = { 'form': None, **type_setting, **lang_setting }
print(settings)
{'spell': 0, 'sound': 0, 'freq': 0}
{'english': 0, 'esperanto': 0, 'french': 0, 'german': 0, 'icelandic': 0, 'russian': 0, 'swahili': 0}
{'form': None, 'spell': 0, 'sound': 0, 'freq': 0, 'english': 0, 'esperanto': 0, 'french': 0, 'german': 0, 'icelandic': 0, 'russian': 0, 'swahili': 0}
In [5]:
vars = list(settings.keys())
print(f"targe var names: {vars}")
d_parts = [ ]
for lang in langs:
local_settings = settings.copy()
print(f"processing: {lang}")
try:
for f in [ f for f in target_files if lang.capitalize() in str(f) ]:
print(f"reading: {f}")
# 言語名の指定
local_settings[lang] = 1
# 型名の指定
for type in vars:
if type in str(f):
local_settings[type] = 1
#
d = pd.read_csv(f, encoding='utf-8', sep = ",", on_bad_lines = 'skip') # Crucially, ...= skip
df = pd.DataFrame(d, columns = vars)
for var in [ var for var in (types + langs) if var != 'freq' ]:
df[var] = local_settings[var]
d_parts.append(df)
except IndexError:
pass
#
if verbose:
d_parts
targe var names: ['form', 'spell', 'sound', 'freq', 'english', 'esperanto', 'french', 'german', 'icelandic', 'russian', 'swahili'] processing: english reading: data-words/base-sound-English-r6e-originals.csv reading: data-words/base-spell-English-r6e-originals.csv processing: esperanto reading: data-words/base-spell-Esperanto-r0-orginal.csv processing: french reading: data-words/base-sound-French-r0-sample900.csv reading: data-words/base-spell-French-r0-originals.csv processing: german reading: data-words/base-sound-German-r1a-original.csv reading: data-words/base-spell-German-r1a-originals.csv processing: icelandic reading: data-words/base-spell-Icelandic-r0-original.csv processing: russian reading: data-words/base-spell-Russian-r0-originals.csv processing: swahili reading: data-words/base-spell-Swahili-r0-orginal.csv
In [6]:
## データ統合
raw_df = pd.concat(d_parts)
raw_df
Out[6]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
15223 rows × 11 columns
In [7]:
## 文字数の列を追加
raw_df['size'] = [ len(x) for x in raw_df[doc_type] ]
raw_df
Out[7]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |
15223 rows × 12 columns
In [8]:
## 言語名= language の列を追加
check = False
language_vals = [ ]
for i, row in raw_df.iterrows():
if check:
print(row)
for j, lang in enumerate(langs):
if check:
print(f"{i}: {lang}")
if row[lang] == 1:
language_vals.append(lang)
if verbose:
print(language_vals)
len(language_vals)
#
raw_df['language'] = language_vals
raw_df
Out[8]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
15223 rows × 13 columns
In [9]:
## 言語の選別
select_languages = True
selected_langs = re.split(r",\s*", "english, french, german, russian, swahili")
print(f"selected languages: {selected_langs}")
if select_languages:
df_new = [ ]
for lang in selected_langs:
df_new.append(raw_df[raw_df[lang] == 1])
raw_df = pd.concat(df_new)
#
raw_df
selected languages: ['english', 'french', 'german', 'russian', 'swahili']
Out[9]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
13673 rows × 13 columns
In [10]:
## 文字数の分布
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.hist(raw_df['size'], bins = 40)
ax.set_xlabel('length of doc')
ax.set_ylabel('freq')
plt.title(f"Length distribution for docs")
fig.show()
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_12571/1088473461.py:12: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown fig.show()
In [11]:
## 長さで濾過
print(f"max doc size: {max_doc_size}")
original_size = len(raw_df)
raw_df = raw_df[raw_df['size'] < max_doc_size]
filtered_size = len(raw_df)
print(f"{original_size - filtered_size} cases removed")
max doc size: 12 365 cases removed
In [12]:
## 結果の検査 1
for lang in langs:
print(raw_df[lang].value_counts())
english 1 8249 0 5059 Name: count, dtype: int64 esperanto 0 13308 Name: count, dtype: int64 french 0 11492 1 1816 Name: count, dtype: int64 german 0 11743 1 1565 Name: count, dtype: int64 icelandic 0 13308 Name: count, dtype: int64 russian 0 12335 1 973 Name: count, dtype: int64 swahili 0 12603 1 705 Name: count, dtype: int64
In [13]:
## 結果の検査 2
for type in types:
print(raw_df[type].value_counts())
spell 1 7588 0 5720 Name: count, dtype: int64 sound 1 11630 0 1678 Name: count, dtype: int64 freq 1 12326 1 966 1 не 1 1 то время как 1 1 северу 1 1 него 1 1 будет 1 1 образом 1 1 мышь 1 Name: count, dtype: int64
In [14]:
## 統合: 割合補正を適用
eng_reduct_factor = 0.2
if balanced:
eng_df = raw_df[raw_df['english'] == 1]
non_eng_df = raw_df[raw_df['english'] == 0]
eng_reduced_df = eng_df.sample(round(len(eng_df) * eng_reduct_factor))
raw_df = pd.concat([eng_reduced_df, non_eng_df])
raw_df
Out[14]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 325 | before | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| 2353 | might | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english |
| 3338 | seek | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | english |
| 1853 | humorous | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | english |
| 2783 | pherson | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
6709 rows × 13 columns
In [15]:
## 結果の検査 3
for lang in langs:
print(raw_df[lang].value_counts())
english 0 5059 1 1650 Name: count, dtype: int64 esperanto 0 6709 Name: count, dtype: int64 french 0 4893 1 1816 Name: count, dtype: int64 german 0 5144 1 1565 Name: count, dtype: int64 icelandic 0 6709 Name: count, dtype: int64 russian 0 5736 1 973 Name: count, dtype: int64 swahili 0 6004 1 705 Name: count, dtype: int64
In [16]:
## 順序のランダマイズ
import sklearn.utils
raw_df = sklearn.utils.shuffle(raw_df)
In [17]:
## データ名の指定
df = raw_df[raw_df[doc_attr] == 1]
print(f"doc_attr: {doc_attr}")
df
doc_attr: sound
Out[17]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 291 | nur | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | german |
| 605 | rangée | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | french |
| 490 | rand | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | german |
| 232 | kœnen | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | german |
| 332 | ʃnɪt | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | german |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3826 | ɹizən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english |
| 2404 | speɪd | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english |
| 3514 | ɪntəɹɛstɪŋ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | english |
| 13 | un | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | french |
| 586 | ɛnthaltn | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 | german |
5031 rows × 13 columns
In [18]:
## ngram の追加
import sys
sys.path.append('..')
import re
import ngrams
import importlib
importlib.reload(ngrams)
import ngrams_skippy
bases = df[doc_type]
## 1gram 列の追加
#sep = r""
#unigrams = [ list(filter(lambda x: len(x) > 0, y)) for y in [ re.split(sep, z) for z in bases ] ]
unigrams = ngrams.gen_unigrams(bases, sep = r"", check = False)
if verbose:
random.sample(unigrams, 5)
#
df['1gram'] = unigrams
#df.loc[:,'1gram'] = unigrams
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_12571/1248262955.py:21: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['1gram'] = unigrams
Out[18]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | 1gram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 291 | nur | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | german | [n, u, r] |
| 605 | rangée | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | french | [r, a, n, g, é, e] |
| 490 | rand | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | german | [r, a, n, d] |
| 232 | kœnen | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | german | [k, œ, n, e, n] |
| 332 | ʃnɪt | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | german | [ʃ, n, ɪ, t] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3826 | ɹizən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [ɹ, i, z, ə, n] |
| 2404 | speɪd | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [s, p, e, ɪ, d] |
| 3514 | ɪntəɹɛstɪŋ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | english | [ɪ, n, t, ə, ɹ, ɛ, s, t, ɪ, ŋ] |
| 13 | un | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | french | [u, n] |
| 586 | ɛnthaltn | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 | german | [ɛ, n, t, h, a, l, t, n] |
5031 rows × 14 columns
In [19]:
## 2gram列の追加
bigrams = ngrams.gen_bigrams(bases, sep = r"", check = False)
## 包括的 2gram の作成
if ngram_is_inclusive:
bigrams = [ [*b, *u] for b, u in zip(bigrams, unigrams) ]
if verbose:
print(random.sample(bigrams, 3))
In [20]:
df['2gram'] = bigrams
if verbose:
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_12571/1480305306.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['2gram'] = bigrams
In [21]:
## 3gram列の追加
trigrams = ngrams.gen_trigrams(bases, sep = r"", check = False)
## 包括的 3gram の作成
if ngram_is_inclusive:
trigrams = [ [ *t, *b ] for t, b in zip(trigrams, bigrams) ]
if verbose:
print(random.sample(trigrams, 3))
In [22]:
df['3gram'] = trigrams
if verbose:
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_12571/3715201492.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['3gram'] = trigrams
In [23]:
## skippy 2grams の生成
import sys
sys.path.append("..") # library path に一つ上の階層を追加
import ngrams_skippy
skippy_2grams = [ ngrams_skippy.generate_skippy_bigrams(x,
missing_mark = '…',
max_distance = max_distance_val, check = False)
for x in df['1gram'] ]
## 包括的 skippy 2-grams の生成
if ngram_is_inclusive:
for i, b2 in enumerate(skippy_2grams):
b2.extend(unigrams[i])
#
if verbose:
random.sample(skippy_2grams, 3)
In [24]:
## skippy 2gram 列の追加
df['skippy2gram'] = skippy_2grams
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_12571/3263801935.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['skippy2gram'] = skippy_2grams
Out[24]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | 1gram | 2gram | 3gram | skippy2gram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 291 | nur | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | german | [n, u, r] | [nu, ur, n, u, r] | [nur, nu, ur, n, u, r] | [nu, n…r, ur, n, u, r] |
| 605 | rangée | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | french | [r, a, n, g, é, e] | [ra, an, ng, gé, ée, r, a, n, g, é, e] | [ran, ang, ngé, gée, ra, an, ng, gé, ée, r, a,... | [ra, r…n, r…g, r…é, r…e, an, a…g, a…é, a…e, ng... |
| 490 | rand | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | german | [r, a, n, d] | [ra, an, nd, r, a, n, d] | [ran, and, ra, an, nd, r, a, n, d] | [ra, r…n, r…d, an, a…d, nd, r, a, n, d] |
| 232 | kœnen | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | german | [k, œ, n, e, n] | [kœ, œn, ne, en, k, œ, n, e, n] | [kœn, œne, nen, kœ, œn, ne, en, k, œ, n, e, n] | [kœ, k…n, k…e, œn, œ…e, œ…n, ne, n…n, en, k, œ... |
| 332 | ʃnɪt | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | german | [ʃ, n, ɪ, t] | [ʃn, nɪ, ɪt, ʃ, n, ɪ, t] | [ʃnɪ, nɪt, ʃn, nɪ, ɪt, ʃ, n, ɪ, t] | [ʃn, ʃ…ɪ, ʃ…t, nɪ, n…t, ɪt, ʃ, n, ɪ, t] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3826 | ɹizən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [ɹ, i, z, ə, n] | [ɹi, iz, zə, ən, ɹ, i, z, ə, n] | [ɹiz, izə, zən, ɹi, iz, zə, ən, ɹ, i, z, ə, n] | [ɹi, ɹ…z, ɹ…ə, ɹ…n, iz, i…ə, i…n, zə, z…n, ən,... |
| 2404 | speɪd | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [s, p, e, ɪ, d] | [sp, pe, eɪ, ɪd, s, p, e, ɪ, d] | [spe, peɪ, eɪd, sp, pe, eɪ, ɪd, s, p, e, ɪ, d] | [sp, s…e, s…ɪ, s…d, pe, p…ɪ, p…d, eɪ, e…d, ɪd,... |
| 3514 | ɪntəɹɛstɪŋ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | english | [ɪ, n, t, ə, ɹ, ɛ, s, t, ɪ, ŋ] | [ɪn, nt, tə, əɹ, ɹɛ, ɛs, st, tɪ, ɪŋ, ɪ, n, t, ... | [ɪnt, ntə, təɹ, əɹɛ, ɹɛs, ɛst, stɪ, tɪŋ, ɪn, n... | [ɪn, ɪ…t, ɪ…ə, ɪ…ɹ, ɪ…ɛ, ɪ…s, ɪ…ɪ, ɪ…ŋ, nt, n…... |
| 13 | un | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | french | [u, n] | [un, u, n] | [u, n, un, u, n] | [un, u, n] |
| 586 | ɛnthaltn | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 | german | [ɛ, n, t, h, a, l, t, n] | [ɛn, nt, th, ha, al, lt, tn, ɛ, n, t, h, a, l,... | [ɛnt, nth, tha, hal, alt, ltn, ɛn, nt, th, ha,... | [ɛn, ɛ…t, ɛ…h, ɛ…a, ɛ…l, ɛ…n, nt, n…h, n…a, n…... |
5031 rows × 17 columns
In [25]:
## skippy 3grams の生成
import sys
sys.path.append("..") # library path に一つ上の階層を追加
import ngrams_skippy
skippy_3grams = [ ngrams_skippy.generate_skippy_trigrams(x,
missing_mark = '…',
max_distance = max_distance_val, check = False)
for x in df['1gram'] ]
## 包括的 skippy 3-grams の生成
if ngram_is_inclusive:
for i, t2 in enumerate(skippy_3grams):
t2.extend(skippy_2grams[i])
#
if verbose:
random.sample(skippy_3grams, 3)
In [26]:
## skippy 3gram 列の追加
df['skippy3gram'] = skippy_3grams
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_12571/1159231133.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['skippy3gram'] = skippy_3grams
Out[26]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | 1gram | 2gram | 3gram | skippy2gram | skippy3gram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 291 | nur | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | german | [n, u, r] | [nu, ur, n, u, r] | [nur, nu, ur, n, u, r] | [nu, n…r, ur, n, u, r] | [nur, nu, n…r, ur, n, u, r] |
| 605 | rangée | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | french | [r, a, n, g, é, e] | [ra, an, ng, gé, ée, r, a, n, g, é, e] | [ran, ang, ngé, gée, ra, an, ng, gé, ée, r, a,... | [ra, r…n, r…g, r…é, r…e, an, a…g, a…é, a…e, ng... | [ran, ra…g, ra…é, ra…e, r…ng, r…n…é, r…n…e, r…... |
| 490 | rand | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | german | [r, a, n, d] | [ra, an, nd, r, a, n, d] | [ran, and, ra, an, nd, r, a, n, d] | [ra, r…n, r…d, an, a…d, nd, r, a, n, d] | [ran, ra…d, r…nd, and, ra, r…n, r…d, an, a…d, ... |
| 232 | kœnen | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | german | [k, œ, n, e, n] | [kœ, œn, ne, en, k, œ, n, e, n] | [kœn, œne, nen, kœ, œn, ne, en, k, œ, n, e, n] | [kœ, k…n, k…e, œn, œ…e, œ…n, ne, n…n, en, k, œ... | [kœn, kœ…e, kœ…n, k…ne, k…n…n, k…en, œne, œn…n... |
| 332 | ʃnɪt | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | german | [ʃ, n, ɪ, t] | [ʃn, nɪ, ɪt, ʃ, n, ɪ, t] | [ʃnɪ, nɪt, ʃn, nɪ, ɪt, ʃ, n, ɪ, t] | [ʃn, ʃ…ɪ, ʃ…t, nɪ, n…t, ɪt, ʃ, n, ɪ, t] | [ʃnɪ, ʃn…t, ʃ…ɪt, nɪt, ʃn, ʃ…ɪ, ʃ…t, nɪ, n…t, ... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3826 | ɹizən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [ɹ, i, z, ə, n] | [ɹi, iz, zə, ən, ɹ, i, z, ə, n] | [ɹiz, izə, zən, ɹi, iz, zə, ən, ɹ, i, z, ə, n] | [ɹi, ɹ…z, ɹ…ə, ɹ…n, iz, i…ə, i…n, zə, z…n, ən,... | [ɹiz, ɹi…ə, ɹi…n, ɹ…zə, ɹ…z…n, ɹ…ən, izə, iz…n... |
| 2404 | speɪd | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [s, p, e, ɪ, d] | [sp, pe, eɪ, ɪd, s, p, e, ɪ, d] | [spe, peɪ, eɪd, sp, pe, eɪ, ɪd, s, p, e, ɪ, d] | [sp, s…e, s…ɪ, s…d, pe, p…ɪ, p…d, eɪ, e…d, ɪd,... | [spe, sp…ɪ, sp…d, s…eɪ, s…e…d, s…ɪd, peɪ, pe…d... |
| 3514 | ɪntəɹɛstɪŋ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | english | [ɪ, n, t, ə, ɹ, ɛ, s, t, ɪ, ŋ] | [ɪn, nt, tə, əɹ, ɹɛ, ɛs, st, tɪ, ɪŋ, ɪ, n, t, ... | [ɪnt, ntə, təɹ, əɹɛ, ɹɛs, ɛst, stɪ, tɪŋ, ɪn, n... | [ɪn, ɪ…t, ɪ…ə, ɪ…ɹ, ɪ…ɛ, ɪ…s, ɪ…ɪ, ɪ…ŋ, nt, n…... | [ɪnt, ɪn…ə, ɪn…ɹ, ɪn…ɛ, ɪn…s, ɪn…t, ɪn…ɪ, ɪn…ŋ... |
| 13 | un | 1 | 1 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | french | [u, n] | [un, u, n] | [u, n, un, u, n] | [un, u, n] | [un, un, u, n] |
| 586 | ɛnthaltn | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 | german | [ɛ, n, t, h, a, l, t, n] | [ɛn, nt, th, ha, al, lt, tn, ɛ, n, t, h, a, l,... | [ɛnt, nth, tha, hal, alt, ltn, ɛn, nt, th, ha,... | [ɛn, ɛ…t, ɛ…h, ɛ…a, ɛ…l, ɛ…n, nt, n…h, n…a, n…... | [ɛnt, ɛn…h, ɛn…a, ɛn…l, ɛn…t, ɛn…n, ɛ…th, ɛ…t…... |
5031 rows × 18 columns
In [27]:
## LDA 構築の基になる document-term matrix (dtm) を構築
from gensim.corpora.dictionary import Dictionary
bots = df[term_type]
diction = Dictionary(bots)
## 結果の確認
print(diction)
Dictionary<1433 unique tokens: ['n', 'nu', 'r', 'u', 'ur']...>
In [28]:
## diction の濾過
import copy
diction_copy = copy.deepcopy(diction)
## filter適用: 実は諸刃の刃で,token数が少ない時には適用しない方が良い
print(f"min freq filter: {term_min_freq}")
print(f"abuse filter: {term_abuse_threshold}")
apply_filter = True
if apply_filter:
diction_copy.filter_extremes(no_below = term_min_freq, no_above = term_abuse_threshold)
## check
print(diction_copy)
min freq filter: 2 abuse filter: 0.05 Dictionary<1085 unique tokens: ['nu', 'ur', 'an', 'gé', 'ng']...>
In [29]:
## Corpus (gensim の用語では corpus) の構築
corpus = [ diction.doc2bow(bot) for bot in bots ]
## check
check = True
if verbose:
sample_n = 5
print(random.sample(corpus, sample_n))
#
print(f"Number of documents: {len(corpus)}")
Number of documents: 5031
In [30]:
## LDA モデルの構築
from gensim.models import LdaModel
#from tqdm import tqdm
## LDAモデル
print(f"Building LDA model with n_topics: {n_topics}")
lda = LdaModel(corpus, id2word = diction, num_topics = n_topics, alpha = 0.01)
#
print(lda) # print(..)しないと中身が見れない
Building LDA model with n_topics: 15 LdaModel<num_terms=1433, num_topics=15, decay=0.5, chunksize=2000>
In [31]:
%%capture --no-display
## LDA のtopic ごとに,関連度の高い term を表示
import pandas as pd
n_terms = 20 # topic ごとに表示する term 数の指定
topic_dfs = [ ]
for topic in range(n_topics):
terms = [ ]
for i, prob in lda.get_topic_terms(topic, topn = n_terms):
terms.append(diction.id2token[ int(i) ])
#
topic_dfs.append(pd.DataFrame([terms], index = [ f'topic {topic+1}' ]))
#
topic_term_df = pd.concat(topic_dfs)
## Table で表示
topic_term_df.T
Out[31]:
| topic 1 | topic 2 | topic 3 | topic 4 | topic 5 | topic 6 | topic 7 | topic 8 | topic 9 | topic 10 | topic 11 | topic 12 | topic 13 | topic 14 | topic 15 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | e | e | a | n | s | k | e | ə | ʁ | d | i | i | a | s | a |
| 1 | n | l | n | a | i | t | a | t | t | n | e | e | l | t | ɛ |
| 2 | c | h | y | k | a | a | r | ɪ | e | o | l | n | t | e | ̃ |
| 3 | t | r | z | t | p | ɔ | n | ɹ | ː | u | r | t | ɔ | o | b |
| 4 | u | c | ʁ | ɔ | ɔ | ɛ | g | s | ə | p | u | m | ɪ | i | p |
| 5 | a | t | e | ʁ | k | ʁ | i | n | ɛ | t | t | v | f | n | ʁ |
| 6 | s | d | wa | d | as | e | s | l | əʁ | nd | il | in | n | l | ɔ |
| 7 | en | o | s | b | l | kɔ | er | a | d | r | o | b | s | a | ɑ̃ |
| 8 | m | n | l | i | si | ̃ | m | m | a | s | ir | l | k | r | e |
| 9 | o | er | w | ɹ | e | n | t | e | m | un | le | ː | ŋ | se | ɑ |
| 10 | r | m | ɛ | ɪ | t | ka | f | ɛ | ̃ | ɪ | v | u | ts | st | d |
| 11 | h | a | ɛl | e | is | s | b | əɹ | f | e | n | r | v | te | ̃s |
| 12 | i | en | ɡ | s | m | i | l | ʃ | ɑ | b | s | ti | z | p | n |
| 13 | nt | w | t | kt | ʁ | at | d | p | i | ʊ | a | ni | ʃ | on | f |
| 14 | p | i | u | ɛ | u | ɪ | o | i | ɔ | ro | f | o | la | ss | t |
| 15 | ch | s | r | ʊ | ̃ | tɛ | re | st | ɑ̃ | pr | d | d | ɪŋ | is | ɛ̃ |
| 16 | ɛ | ch | ʁa | p | z | p | ge | d | l | bo | re | mi | va | la | ʁa |
| 17 | co | he | i | l | ɛ | ʏ | an | ən | ʁɛ | ou | m | q | ɡ | as | s |
| 18 | ce | te | o | m | j | ke | ar | u | ʁe | po | ll | iv | m | b | aɛ |
| 19 | b | u | c | o | ɔ̃ | f | ra | eɪ | n | ː | é | s | an | u | bɛ |
In [32]:
%%capture --no-display
## pyLDAvis を使った結果 LDA の可視化: 階層クラスタリングより詳しい
import pyLDAvis
#installed_version = sys.version
installed_version = pyLDAvis.__version__
print(f"installed_version: {installed_version}")
if float(installed_version[:3]) > 3.1:
import pyLDAvis.gensim_models as gensimvis
else:
import pyLDAvis.gensim as gensimvis
#
pyLDAvis.enable_notebook()
#
lda_used = lda
corpus_used = corpus
diction_used = diction
## 実行パラメター
use_tSNE = False
if use_tSNE:
vis = gensimvis.prepare(lda_used, corpus_used, diction_used, mds = 'tsne',
n_jobs = 1, sort_topics = False)
else:
vis = gensimvis.prepare(lda_used, corpus_used, diction_used,
n_jobs = 1, sort_topics = False)
#
pyLDAvis.display(vis)
## 結果について
## topic を表わす円の重なりが多いならn_topics が多過ぎる可能性がある.
## ただし2Dで重なっていても,3Dなら重なっていない可能性もある
Out[32]:
In [33]:
## LDA がD に対して生成した topics の弁別性を確認
## 得られたtopics を確認
topic_dist = lda.get_topics()
if verbose:
topic_dist
In [34]:
## 検査 1: topic ごとに分布の和を取る
print(topic_dist.sum(axis = 1))
[1. 0.99999994 1. 1. 0.99999994 0.99999994 1. 0.99999994 1. 1. 1. 0.99999994 1. 0.9999999 1. ]
In [35]:
## 検査 2: 総和を求める: n_topics にほぼ等しいなら正常
print(topic_dist.sum())
15.0
In [36]:
## term エンコード値の分布を確認
import matplotlib.pyplot as plt
plt.figure(figsize = (4,5))
sampling_rate = 0.3
df_size = len(topic_dist)
sample_n = round(df_size * sampling_rate)
topic_sampled = random.sample(list(topic_dist), sample_n)
T = sorted([ sorted(x, reverse = True) for x in topic_sampled ])
plt.plot(T, range(len(T)))
plt.title("Distribution of sorted values ({sample_n} samples) for topic/term encoding")
plt.show()
In [37]:
## tSNE を使った topics のグループ化 (3D)
from sklearn.manifold import TSNE
import numpy as np
## tSNE のパラメターを設定
## n_components は射影先の空間の次元: n_components = 3 なら3次元空間に射影
## perplexity は結合の強さを表わす指数で,値に拠って結果が代わるので,色々な値を試すと良い
#perplexity_val = 10 # 大き過ぎると良くない
top_perplexity_reduct_rate = 0.3
perplexity_val = round(len(topic_dist) * top_perplexity_reduct_rate)
topic_tSNE_3d = TSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)
## データに適用
top_tSNE_3d_fitted = topic_tSNE_3d.fit_transform(np.array(topic_dist))
In [38]:
## Plotlyを使って tSNE の結果の可視化 (3D)
#import plotly.express as pex
import plotly.graph_objects as go
import numpy as np
top_tSNE = top_tSNE_3d_fitted
fig = go.Figure(data = [go.Scatter3d(x = top_tSNE[:,0], y = top_tSNE[:,1], z = top_tSNE[:,2],
mode = 'markers')])
## 3D 散布図にラベルを追加する処理は未実装
title_val = f"3D tSNE view for LDA (#topics: {n_topics}, doc: {doc_type}, term: {term_size} {term_type})"
fig.update_layout(autosize = False,
width = 600, height = 600, title = title_val)
fig.show()
In [39]:
## 構築した LDA モデルを使って文(書)を分類する
## .get_document_topics(..) は minimu_probability = 0としないと
## topic の値が小さい場合に値を返さないので,
## パラメター
ntopics = n_topics # LDA の構築の最に指定した値を使う
check = False
encoding = [ ]
for i, row in df.iterrows():
if check:
print(f"row: {row}")
doc = row[doc_type]
bot = row[term_type]
## get_document_topics(..) では minimu_probability = 0 としないと
## 値が十分に大きな topics に関してだけ値が取れる
enc = lda.get_document_topics(diction.doc2bow(bot), minimum_probability = 0)
if check:
print(f"enc: {enc}")
encoding.append(enc)
#
len(encoding)
Out[39]:
5031
In [40]:
## enc 列の追加
#df['enc'] = np.array(encoding) # This flattens arrays
#df['enc'] = list(encoding) # ineffective
df['enc'] = [ list(map(lambda x: x[1], y)) for y in encoding ]
if verbose:
df['enc']
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_12571/1047258704.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [41]:
## エンコーディングのstd の分布を見る
from scipy.stats import tstd
from matplotlib import pyplot as plt
plt.figure(figsize = (6,4))
std_data = [ tstd(x) for x in df['enc'] ]
plt.hist(std_data)
plt.title("Distribution of standard deviations")
plt.show()
In [42]:
## doc のエンコーディング
## 一様分布の事例を除外
from scipy.stats import tstd # standard deviation の計算用
print(f"{len(df)} instances before filtering")
check = False
doc_enc = df['enc']
max_std = max([ tstd(x) for x in doc_enc])
if check: print(f"std max: {max_std}")
min_std = min([ tstd(x) for x in doc_enc])
if check: print(f"std min: {min_std}")
first_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-0]
print(f"std 1st min: {first_min_std}")
second_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-1]
print(f"std 2nd min: {second_min_std}")
5031 instances before filtering std 1st min: 0.0 std 2nd min: 0.25636767271560224
In [43]:
## df_filtered の定義
## 閾値は2番目に小さい値より小さく最小値よりは大きな値であるべき
std_threshold = second_min_std / 4 # 穏健な値を得るために4で割った
print(f"std_threshold: {std_threshold}")
## Rっぽい次のコードは通らない
#df_filtered = df[ df['encoding'] > std_threshold ]
## 通るのは次のコード: Creating a list of True/False and apply it to DataFrame
std_tested = [ False if tstd(x) < std_threshold else True for x in df['enc'] ]
df_filtered = df[ std_tested ]
#
print(f"{len(df_filtered)} instances after filtering ({len(df) - len(df_filtered)} instances removed)")
std_threshold: 0.06409191817890056 5030 instances after filtering (1 instances removed)
In [44]:
## doc エンコード値の分布を確認
sample_n = 50
E = sorted([ sorted(x, reverse = True) for x in df_filtered['enc'].sample(sample_n) ])
plt.figure(figsize = (5,5))
plt.plot(E, range(len(E)))
plt.title(f"Distribution of sorted encoding values for sampled {sample_n} docs")
plt.show()
In [45]:
len(df_filtered['language'])
Out[45]:
5030
In [46]:
## tSNE 用の事例サンプリング = tSNE_df の定義
tSNE_sampling = True
tSNE_sampling_rate = 0.33
if tSNE_sampling:
tSNE_df_original = df_filtered.copy()
sample_n = round(len(tSNE_df_original) * tSNE_sampling_rate)
tSNE_df = tSNE_df_original.sample(sample_n)
print(f"tSNE_df has {len(tSNE_df)} rows after sampling")
else:
tSNE_df = df_filtered
tSNE_df has 1660 rows after sampling
In [47]:
tSNE_df.columns
Out[47]:
Index(['form', 'spell', 'sound', 'freq', 'english', 'esperanto', 'french',
'german', 'icelandic', 'russian', 'swahili', 'size', 'language',
'1gram', '2gram', '3gram', 'skippy2gram', 'skippy3gram', 'enc'],
dtype='object')
In [48]:
## tSNE の結果の可視化: Plotly を使った 3D 描画
import numpy as np
from sklearn.manifold import TSNE as tSNE
import plotly.express as pex
import plotly.graph_objects as go
import matplotlib.pyplot as plt
## tSNE のパラメターを設定
perplexity_max_val = round(len(tSNE_df)/4)
for perplexity_val in range(5, perplexity_max_val, 30):
## tSNE 事例の生成
tSNE_3d_varied = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)
## データに適用
doc_enc = np.array(list(tSNE_df['enc']))
doc_tSNE_3d_varied = tSNE_3d_varied.fit_transform(doc_enc)
T = zip(doc_tSNE_3d_varied[:,0], doc_tSNE_3d_varied[:,1], doc_tSNE_3d_varied[:,2],
tSNE_df['language']) # zip(..)が必要
df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])
## 作図
fig = go.Figure()
for lang in np.unique(df['language']):
part = df[df['language'] == lang]
fig.add_trace(
go.Scatter3d(
x = part['D1'], y = part['D2'], z = part['D3'],
name = lang, mode = 'markers', marker = dict(size = 6),
showlegend = True
)
)
title_val = f"tSNE 3D map (ppl: {perplexity_val}) of {doc_attr}s encoded by LDA ({n_topics} topics; term: {term_type})"
fig.update_layout(title = dict(text = title_val),
autosize = False, width = 600, height = 600,)
fig.show()
In [49]:
## 階層クラスタリングのための事例のサンプリング
hc_sampling_rate = 0.1 # 大きくし過ぎると図が見にくい
df_size = len(tSNE_df)
hc_sample_n = round(df_size * hc_sampling_rate)
hc_df = tSNE_df.sample(hc_sample_n)
##
print(f"{hc_sample_n} rows are sampled")
hc_df['language'].value_counts()
166 rows are sampled
Out[49]:
language french 59 english 59 german 48 Name: count, dtype: int64
In [50]:
## doc 階層クラスタリングの実行
import numpy as np
import plotly
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
## 距離行列の生成
Enc = list(hc_df['enc'])
linkage = linkage(Enc, method = 'ward', metric = 'euclidean')
## 描画サイズの指定
plt.figure(figsize = (5, round(len(hc_df) * 0.15))) # This needs to be run here, before dendrogram construction.
## 事例ラベルの生成
label_vals = [ x[:max_doc_size] for x in list(hc_df[doc_type]) ] # truncate doc keys
## 樹状分岐図の作成
dendrogram(linkage, orientation = 'left', labels = label_vals, leaf_font_size = 7)
## 描画
plt.title(f"Hierarchical clustering of (sampled) {len(hc_df)} (= {100 * hc_sampling_rate}%) {doc_attr}s as docs\n \
encoded via LDA ({n_topics} topics) with {term_type} as terms")
## ラベルに language に対応する色を付ける
lang_colors = { lang_name : i for i, lang_name in enumerate(np.unique(hc_df['language'])) }
ax = plt.gca()
for ticker in ax.get_ymajorticklabels():
form = ticker.get_text()
row = hc_df.loc[hc_df[doc_type] == form]
#lang = row['language']
lang = row['language'].to_string().split()[-1] # trick
try:
lang_id = lang_colors[lang]
except (TypeError, KeyError):
print(f"color encoding error at: {lang}")
#
ticker.set_color(plotly.colors.qualitative.Plotly[lang_id]) # id の基数調整
#
plt.show()
In [51]:
## tSNE の結果の可視化 (2D)
#import seaborn as sns
import numpy as np
import plotly
import plotly.express as pex
import matplotlib.pyplot as plt
from adjustText import adjust_text
## tSNE 事例の生成
perplexity_selected = 250
tSNE_3d = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_selected, n_iter = 1000)
## データに適用
doc_enc = np.array(list(tSNE_df['enc']))
doc_tSNE_3d = tSNE_3d.fit_transform(doc_enc)
T = zip(doc_tSNE_3d[:,0], doc_tSNE_3d[:,1], doc_tSNE_3d[:,2],
tSNE_df['language']) # zip(..)が必要
df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])
## 描画
plt.figure(figsize = (5, 5))
plt.set_colors = pex.colors.qualitative.Plotly
for r in [ np.roll([0,1,2], -i) for i in range(0,3) ]:
if check:
print(r)
X, Y = df.iloc[:,r[0]], df.iloc[:,r[1]]
gmax = max(X.max(), Y.max())
gmin = min(X.min(), Y.min())
plt.xlim(gmin, gmax)
plt.ylim(gmin, gmax)
colormap = pex.colors.qualitative.Plotly
lang_list = list(np.unique(tSNE_df['language']))
cmapped = [ colormap[lang_list.index(lang)] for lang in df['language'] ]
scatter = plt.scatter(X, Y, s = 40, c = cmapped, edgecolors = 'w')
## 文字を表示する事例のサンプリング
lab_sampling_rate = 0.02
lab_sample_n = round(len(tSNE_df) * lab_sampling_rate)
sampled_keys = [ doc[:max_doc_size] for doc in random.sample(list(tSNE_df[doc_type]), lab_sample_n) ]
## labels の生成
texts = [ ]
for x, y, s in zip(X, Y, sampled_keys):
texts.append(plt.text(x, y, s, size = 9, color = 'blue'))
## label に repel を追加: adjustText package の導入が必要
adjust_text(texts, force_points = 0.2, force_text = 0.2,
expand_points = (1, 1), expand_text = (1, 1),
arrowprops = dict(arrowstyle = "-", color = 'black', lw = 0.5))
#
plt.title(f"tSNE (ppl: {perplexity_selected}) 2D map of {len(tSNE_df)} {doc_attr}s via LDA ({term_type}; {n_topics} topics)")
#plt.legend(np.unique(cmapped))
plt.show()
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